November 15, 2021

Every conference day consists of three presentation blocks, followed by a keynote talk in the evening. Times are Central European Time (CET). Registered participants will receive a link to Zoom to join the Meeting.

Paul Hünermund, Jermain Kaminski, Carla Schmitt, Beyers Louw
Copenhagen Business School & Maastricht University
Session 1
10:40Self-fulfilling Bandits: Endogeneity spillover and dynamic selection in algorithmic decision-making
Xiaowei Zhang
Hongkong University
10:55Off-policy learning of dynamic content promotions
Joel Persson
ETH Zürich
11:10Estimating returns to special education: Combining machine learning and text analysis to address confounding
Aurélien Sallin
St. Gallen University
11:25What’s on the telly? Causality for recommender systems in public-service media corporations
Jordi Mur
University of Barcelona
11:40Q & A
12:0060 min break (Timer)
Session 2
13:00Structural causal models are (solvable by) credal networks
Alessandro Antonucci
Dalle Molle Institute for Artificial Intelligence Research (IDSIA)
13:15Estimating the probabilities of causation via deep monotonic twin networks
Ciarán Lee
Spotify Research
13:30Double machine learning for sample selection models
Martin Huber
University of Fribourg
13:45Positivity violation detection and explainability
Hanan Shteingart
14:00Q & A
14:2030 min break (Timer)
Session 3
14:50Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment
Jason Poulos
Harvard Medical School
15:05Crime and mismeasured punishment: Marginal treatment effect with misclassification
Vitor Possebom
Yale University
15:20When should we (not) interpret linear IV estimands as LATE?
Tymon Sloczynski
Brandeis University
15:35Preferences and productivity in organizational matching: Theory and empirics from internal labor markets
Bo Cowgill
Columbia Business School
15:50Q & A
16:1030 min break (Timer)
Session 4
16:40Experimentation and startup performance: Evidence from A/B testing
Rem Koning
Harvard Business School
16:55The paper of how: Estimating treatment effects using the front-door criterion
Marc Bellemare
University of Minnesota
17:10Causal-driven machine learning at Uber scale: A case study
Okke van der Wal
17:25Generalizing experimental results by leveraging knowledge of mechanisms
Carlos Cinelli
University of Washington
17:40Q & A
18:0030 min break (Timer)
Sara Magliacane
University of Amsterdam & MIT-IBM Watson AI Lab